Understanding JSON Data in Snowflake SQL

As data scientists and analysts, we often encounter complex data formats that require specialized techniques to extract insights. One such format is JSON (JavaScript Object Notation), which has become increasingly popular for storing structured data. In this article, we’ll delve into how to work with JSON data in Snowflake SQL, specifically focusing on extracting column names with special characters (@) and values denoted by a dollar sign ($).

Introduction to Snowflake SQL

Snowflake is a cloud-native relational database management system that offers a modern, scalable, and highly performant way of storing and querying data. Its SQL syntax is similar to traditional databases like MySQL or PostgreSQL, making it easy for developers familiar with these languages to transition to Snowflake.

Working with JSON Data in Snowflake

Snowflake provides several ways to work with JSON data, including the parse_json function, which can be used to parse a JSON string into a table. However, when dealing with complex or unstructured data, this approach may not yield the desired results.

In our example, we’re working with an entire column of unstructured data in the form of a JSON array. Our goal is to extract specific attributes from this data and perform operations like flattening, pivoting, and aggregation.

Using GET Functions to Extract Column Names and Values

One approach to extracting column names and values from our JSON data is by using Snowflake’s GET functions. These functions allow us to access values within a JSON array or object in a flexible and efficient manner.

The syntax for the GET function is as follows:

GET(json_array, 'key')::string

where json_array is the input JSON data, and key is the attribute we want to extract. The ::string cast ensures that the result is returned as a string.

For example, to extract the value denoted by the $ symbol in our JSON array, we can use the following query:

SELECT GET(a.value, '$')::string AS val FROM json_array;

Similarly, to extract the attribute denoted by the @ symbol, we can use the following query:

SELECT GET(a.value, '@')::string AS attr FROM json_array;

Flattening and Pivoting JSON Data

Once we’ve extracted our column names and values using the GET function, we need to perform operations like flattening and pivoting. Flattening involves expanding a hierarchical structure into a flat table, while pivoting involves rotating the columns so that they become the new rows.

In our example, we’re using Snowflake’s json_cte (Common Table Expression) feature to create a temporary table that represents our JSON array. We then use the lateral flatten clause to expand this array into separate rows, which allows us to perform operations like aggregation and pivoting.

The syntax for the flatten clause is as follows:

FROM json_array LATERAL FLATTEN(input => ...)

where json_array is our input JSON data, and input represents the flattened array of values.

Once we’ve expanded our JSON array into separate rows using the flatten clause, we can use Snowflake’s pivot function to rotate the columns. The syntax for the pivot function is as follows:

SELECT *
FROM table_name
PIVOT (
  MAX(val) FOR attr IN ('agon', 'aged')
)

where table_name represents our temporary table that contains the flattened and pivoted data.

Full Example Query

Here’s a full example query that demonstrates how to extract column names with special characters (@) and values denoted by a dollar sign ($), as well as flatten and pivot JSON data:

with json_cte as (
  select parse_json('[
    {
      "$": "5.1.0.18",
      "@": "agon"
    },
    {
      "$": "199891e7-d75c",
      "@": "aged"
    }
  ]') as json_array
)
, attributes (
  select
    GET(a.value, '@')::string attr,
    GET(a.value, '$')::string val
  from json_cte,
  lateral flatten(input => json_array) a
)

select *
from attributes  
pivot(max(val) for attr in ('agon', 'aged'));

This query uses the json_cte feature to create a temporary table that represents our JSON array. It then uses the lateral flatten clause to expand this array into separate rows, which allows us to perform operations like aggregation and pivoting.

Finally, it uses Snowflake’s pivot function to rotate the columns, producing the desired output with flattened and pivoted data.

Conclusion

In conclusion, working with JSON data in Snowflake SQL requires a combination of specialized functions, clever use of syntax features, and attention to detail. By leveraging the GET functions, flatten, and pivot clauses, we can extract column names with special characters (@) and values denoted by a dollar sign ($), as well as perform operations like flattening and pivoting.

Whether you’re working with complex or unstructured data, Snowflake’s JSON capabilities offer a powerful way to unlock insights from your data. By mastering these advanced techniques, you can unlock the full potential of Snowflake and drive business value from your data.

Last modified on 2024-12-01